Conventionally, there is the error diffusion method (“An adaptive algorithm for spatial gray scale”, SID International Symposium Digest of Technical Papers, vol4.3, 1975, pp.36–37) by R. Floyd et al. as means for converting a multivalued image data into a binary image (or an image that has a fewer levels of gray scale than the inputted levels of gray scale). The error diffusion method diffuses a binarization error generated in a pixel to a plurality of pixels thereafter so as to artificially represent a gray scale. While this error diffusion method allows binarization of high image quality, it has a fault that excessive processing time is required. In the case of performing binarization by using a density pattern method wherein a pixel of the multivalued image is represented by a plurality of binary pixels, high-speed binarization can be performed. However, in this case the error arising cannot be propagated and limitation to gray scale representation arises, and so a problem remains in terms of the image quality.
It is possible to acquire a high-speed and high-quality binary image by using an image processing apparatus characterized by, as disclosed in U.S. Pat. No. 5,638,188, having input means for inputting multivalued data, computing means for computing error corrected value by adding error data to the input multivalued data, selection means for selecting a predetermined dot pattern based on the above described error corrected value, error computing means for computing a difference between a predetermined value assigned for each of the dot pattern and the above described error corrected value, and storage means for storing the above described difference in memory as error data.
The conventional error diffusion method uses a fixed weight (a diffusion coefficient) when diffusing a binarization error regardless of the input value. However, the conventional method has a problem that the dots are generated successively in a chain-like manner without being evenly distributed in highlight and shadow regions. For this reason, even with the above image processing apparatus, desirable results cannot be acquired, as the problem of the error diffusion method affects the output binary image. Moreover, in the case where the above dot pattern and error diffusion coefficient are selected separately, there is a problem that their combination may not yield the desirable binarization results.
In addition, as a related conventional technology, the error diffusion method wherein different diffusion coefficients are used according to input gray level value is disclosed in Japanese Patent Laid-Open No. 10-312458.
This method defines a first diffusion coefficient for highlight and shadow regions, and a second diffusion coefficient for a mid-tone region. The two diffusion coefficients are linear-interpolated to be used for the transition region between highlights and mid-tones, and for the transition region between mid-tones and shadows.
However, in the above conventional method, while the diffusion coefficients for highlight and shadow regions and for mid-tone regions are defined, diffusion coefficients for other regions are obtained by linear-interpolating the two diffusion coefficients. Therefore optimum diffusion coefficients are not defined for all levels of gray scale. In addition, there are no grounds even for the gray levels of which diffusion coefficients are exactly defined, that the defined diffusion coefficients are optimum. Furthermore, there is a problem that the optimum diffusion coefficients depend on conditions such as resolution of the image.